To accomplish this task, we adopt a simple variant of slot attention [1]},
where instead of Gaussian-initialized slots, we use learnable query vectors.
Slot attention has recently shown remarkable performance for object-centric representation learning,
where the query vectors compete to explain parts of the inputs
via a softmax-based attention mechanism,
and the representations of these slots are iteratively updated with a recurrent update function.
In our case of motion segmentation,
ideally,
the final representation in each query vector separately encodes the moving object or the background,
which can then be decoded and combined to reconstruct the input flow fields.
